AI Engineer

Techila Global; Bengaluru; Hybrid; INR 2,787,000 - 3,849,000; Full time

Job summary

Techila Global is seeking an AI Engineer with 8–10 years of experience to join our team of Salesforce experts in a hybrid role across Chennai and Bangalore. You will help develop AI-driven solutions, build PoCs and automation workflows, and integrate LLM/AI services with enterprise applications.

The role demands hands-on Python, API development, and cloud AI service knowledge (Azure/OpenAI/AWS). Strong architectural thinking and production deployment experience are essential.

Qualifications

• Experience building AI-driven solutions and integrating LLM/AI services into enterprise apps.
• Proven ability to designPoCs, automation workflows, and production-ready AI components.
• Strong knowledge of embeddings, retrieval, and prompt strategies is a plus.

Responsibilities

• Support the development and integration of AI-powered solutions.
• Build PoCs and automation workflows to streamline client deployments.
• Integrate LLM/AI services with enterprise applications and data sources.

Skills

Python

APIs

AI/ML integration

LLMs

Prompt engineering

RAG frameworks

Cloud AI services

Job description

Hybrid Bangalore, Chennai 8–10 yrs Apply by Oct 14, 2026AI EngineerJoin Techila's team of Salesforce experts. We build senior-led transformations that deliver measurable outcomes for clients worldwide.Experience8–10 yrsEmployment TypeFull-timeOpenings1 positionApply ByOct 14, 2026 Required SkillsAI EngineerJob DescriptionJob Description ManualAI Kindly share Immediate Joiners for the following position: Position Name Req ID Experience Location Duration Budget AI Engineer 723234 Senior | Senior Level 2 Chennai/Bangalore (India) 9 Months 25 USD/Hourly Support the development and integration of AI-driven solutions, build PoCs and automation workflows, and integrate LLM/AI services with enterprise applications. Required Skills Python, APIs, AI/ML integration, LLMs, prompt engineering, RAG frameworks, and Cloud AI services (Azure/OpenAI/AWS). Question Average Candidate Will Say Good Candidate Will Say Red Flags Tell me about a GenAI/LLM project you have built. Walk me through the architecture and your contribution. Explains the business use case and mentions using GPT/OpenAI. Can describe some components but lacks depth on architecture. Clearly explains end-to-end architecture, including data flow, APIs, retrieval layer, LLM integration, deployment, and personal contributions. Can justify design decisions. Cannot explain architecture. Only discusses prompts or business requirements. Uses buzzwords without understanding. Contribution is unclear (\"my team built it\"). Strong AI Engineer Indicators Has built at least one LLM/RAG application end-to-end Comfortable with Python and API development Can explain embeddings, chunking, retrieval, and prompt engineering Has exposure to Azure OpenAI, OpenAI APIs, AWS Bedrock, or similar services Understands deployment, monitoring, and operationalization of AI systems Can discuss trade-offs and production challenges 2. If you need to build a chatbot over enterprise documents, how would you approach it? Mentions document upload, embeddings, and vector database at a high level. Understands the concept of RAG but lacks implementation details. Explains document ingestion, chunking strategy, embeddings, vector store, retrieval, metadata filtering, prompt augmentation, security/access control, and response grounding. Suggests directly uploading documents to ChatGPT. Cannot explain RAG or embeddings. No understanding of retrieval mechanisms. No consideration for enterprise security. Immediate Concerns Experience limited to using ChatGPT UI only No hands-on coding experience with AI frameworks Cannot explain architecture or data flow No API integration experience No understanding of RAG, embeddings, or vector databases Cannot describe any real project ownership or production deployment 3. What frameworks have you used for AI orchestration (LangChain, LangGraph, Semantic Kernel, etc.)? Why did you choose them? Has used one or more frameworks and can explain basic workflow implementation. Explains orchestration patterns, agent workflows, tool calling, state management, multi-agent coordination, and reasons for selecting a specific framework over alternatives. Knows framework names only. Cannot explain why it was selected. Cannot differentiate orchestration frameworks from LLM APIs. No practical usage. 4. Tell me about a challenge you faced while working with an LLM application and how you resolved it. Mentions common issues such as hallucinations or response quality and describes basic prompt tuning. Discusses a real production challenge (hallucinations, retrieval quality, latency, token limits, cost, deployment issues, etc.), root cause analysis, and measurable improvements achieved. Claims there were no challenges. Gives generic blog-level answers. No troubleshooting experience. Cannot explain resolution approach. 5. How have you deployed or integrated AI applications in Azure, AWS, or enterprise environments? Has worked with cloud AI services and can describe basic deployment or API integration. Explains deployment architecture, CI/CD, secrets management, monitoring, logging, scaling, networking, security, and integration with enterprise applications. Only local development experience. No cloud exposure. Cannot explain deployment lifecycle. No understanding of operationalization. 6. Explain a RAG implementation you worked on. How was the retrieval layer designed? \"We stored documents in a vector database and searched them.\" Explains chunking strategy, embeddings, vector database, retrieval process, reranking, metadata filtering, evaluation metrics, and hallucination mitigation. Example: \"We used Azure AI Search with metadata filters and hybrid search to improve precision.\" Cannot explain embeddings. Cannot explain chunking. Thinks RAG is simply uploading PDFs to ChatGPT. No retrieval strategy discussion.At a GlanceWork ModeHybridEmploymentFull-timeExperience8–10 yrsOpenings1LocationBangalore, ChennaiDeadlineOct 14, 2026[ Hiring process ]What to expectFour stages, typically completed within 2–3 weeks. We respect your time — every stage has a clear purpose and timely feedback.STEP 0130 minScreening CallIntroductory conversation with our talent team to understand your background and motivations.STEP 0260–90 minTechnical RoundLive problem-solving with a senior architect on Salesforce design, integrations, or domain depth.STEP 0345 minCulture FitConversation with practice leadership covering working style, ownership, and how you collaborate.STEP 04Within 5 daysOfferFormal offer with full compensation breakdown, start date, and onboarding plan.